Copyright (c) 2009 University of Pennsylvania All rights reserved. http://repository.upenn.edu/be_papers Recent documents in Departmental Papers (BE) en-us Wed, 28 Oct 2009 23:19:19 PDT 3600 http://repository.upenn.edu/be_papers/149 http://repository.upenn.edu/be_papers/149 Tue, 27 Oct 2009 12:12:20 PDT Molecular beacons (MBs) have shown great potential for the imaging of RNAs within single living cells; however, the ability to perform accurate measurements of RNA expression can be hampered by false-positives resulting from nonspecific interactions and/or nuclease degradation. These false-positives could potentially be avoided by introducing chemically modified oligonucleotides into the MB design. In this study, fluorescence microscopy experiments were performed to elucidate the subcellular trafficking, false-positive signal generation, and functionality of 2'-O-methyl (2Me) and 2'-O-methyl-phosphorothioate (2MePS) MBs. The 2Me MBs exhibited rapid nuclear sequestration and a gradual increase in fluorescence over time, with nearly 50% of the MBs being opened nonspecifically within 24 h. In contrast, the 2MePS MBs elicited an instantaneous increase in false-positives, corresponding to ~5-10% of the MBs being open, but little increase was observed over the next 24 h. Moreover, trafficking to the nucleus was slower. After 24 h, both MBs were localized in the nucleus and lysosomal compartments, but only the 2MePS MBs were still functional. When the MBs were retained in the cytoplasm, via conjugation to NeutrAvidin, a significant reduction in false-positives and improvement in functionality was observed. Overall, these results have significant implications for the design and applications of MBs for intracellular RNA measurement. Anthony K. Chen http://repository.upenn.edu/be_papers/148 http://repository.upenn.edu/be_papers/148 Mon, 14 Sep 2009 12:53:22 PDT Alterations in the expression, molecular composition, and localization of voltage-gated sodium channels play major roles in a broad range of neurological disorders. Recent evidence identifies sodium channel proteolysis as a key early event after ischemia and traumatic brain injury, further expanding the role of the sodium channel in neurological diseases. In this study, we investigate the protease responsible for proteolytic cleavage of voltage-gated sodium channels (NaChs). NaCh proteolysis occurs after protease activation in rat brain homogenates, pharmacological disruption of ionic homeostasis in cortical cultures, and mechanical injury using an in vitro model of traumatic brain injury. Proteolysis requires Ca2+ and calpain activation but is not influenced by caspase-3 or cathepsin inhibition. Proteolysis results in loss of the full-length {alpha}-subunits, and the creation of fragments comprising all domains of the channel that retain interaction even after proteolysis. Cell surface biotinylation after mechanical injury indicates that proteolyzed NaChs remain in the membrane before noticeable evidence of neuronal death, providing a mechanism for altered action potential initiation, propagation, and downstream signaling events after Ca2+ elevation. Catherine R. von Reyn http://repository.upenn.edu/be_papers/147 http://repository.upenn.edu/be_papers/147 Wed, 09 Sep 2009 10:55:53 PDT We present a computational methodology for incorporating thermal effects and calculating relative free energies for elastic fluid membranes subject to spatially dependent intrinsic curvature fields using the method of thermodynamic integration. Based on a simple model for the intrinsic curvature imposed only in a localized region of the membrane, we employ thermodynamic integration to calculate the free-energy change as a function of increasing strength of the intrinsic curvature field and a thermodynamic cycle to compute free-energy changes for different sizes of the localized region. By explicitly computing the free-energy changes and by quantifying the loss of entropy accompanied with increasing membrane deformation, we show that the membrane stiffness increases with increasing intrinsic field, thereby, renormalizing the membrane bending rigidity. The second main conclusion of this work is that the entropy of the membrane decreases with increasing size of the localized region subject to the curvature field. Our results help to quantify the free-energy change when a planar membrane deforms under the influence of curvature-inducing proteins at a finite temperature. Neeraj J. Agrawal http://repository.upenn.edu/be_papers/146 http://repository.upenn.edu/be_papers/146 Wed, 02 Sep 2009 10:44:19 PDT Brittany Coats http://repository.upenn.edu/be_papers/145 http://repository.upenn.edu/be_papers/145 Mon, 24 Aug 2009 09:46:17 PDT Prostate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high vs. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations. Yangming Ou http://repository.upenn.edu/be_papers/144 http://repository.upenn.edu/be_papers/144 Mon, 24 Aug 2009 08:57:45 PDT Rationale and Objectives:Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. MRI is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques like perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multi-parametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods, in order to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter.Materials and Methods:Five structural MR sequences, namely, B0, Diffusion Weighted Images, FLAIR, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI, i.e., fractional anisotropy and apparent diffusion coefficient, are used to create an intensity-based tissue profile. This is incorporated into a non-linear pattern classification technique to create a multi-parametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high grade neoplasms who have not received any therapy prior to imaging.Results:Preliminary results demonstrate that this multi-parametric tissue characterization helps to better differentiate between neoplasm, edema and healthy tissue, and to identify tissue that is likely progress to neoplasm in the future. This has been validated on expert assessed tissue.Conclusion:This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence. Raginia Verma http://repository.upenn.edu/be_papers/143 http://repository.upenn.edu/be_papers/143 Mon, 24 Aug 2009 08:48:28 PDT Rationale and ObjectivesNeedle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High contrast MR imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histological images. Therefore, it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this paper is to develop a registration technique for aligning histological and MR images of the same prostate.Material and MethodsFive pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient blob-like structures of histological and MR images.ResultsOur developed registration method can accurately register histological and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods.ConclusionsWe have developed a novel method for deformable registration of histological and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histological and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathological information, which is valuable for various clinical studies. Yiqiang Zhan http://repository.upenn.edu/be_papers/142 http://repository.upenn.edu/be_papers/142 Mon, 24 Aug 2009 08:41:57 PDT A general-purpose deformable registration algorithm referred to as "DRAMMS" is presented in this paper. DRAMMS adds to the literature of registration methods that bridge between the traditional voxel-wise methods and landmark/feature-based methods. In particular, DRAMMS extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each voxel in a multi-scale and multi-resolution fashion. Compared with intensity or mutual-information based methods, the high-dimensional optimal Gabor attributes render different anatomical regions relatively distinctively identifiable and therefore help establish more accurate and reliable correspondence. Moreover, the optimal Gabor attribute vector is constructed in a way that generalizes well, i.e., it can be applied to different registration tasks, regardless of the image contents under registration. A second characteristic of DRAMMS is that it is based on a cost function that weights different voxel pairs according to a metric referred to as "mutual-saliency", which reflects the uniqueness (reliability) of anatomical correspondences implied by the tentative transformation. As a result, image voxels do not contribute equally to the optimization process, as in most voxel-wise methods, or in a binary selection fashion, as in most landmark/feature-based methods. Instead, they contribute according to a continuously-valued mutual-saliency map, which is dynamically updated during the algorithm's evolution. The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate. Yangming Ou http://repository.upenn.edu/be_papers/141 http://repository.upenn.edu/be_papers/141 Thu, 20 Aug 2009 11:27:31 PDT This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal bloblike structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems - segmentation and registration - inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies. Yangming Ou http://repository.upenn.edu/be_papers/140 http://repository.upenn.edu/be_papers/140 Thu, 20 Aug 2009 11:27:28 PDT Prostate biopsy is the gold standard procedure for pre-operatively estimating Gleason Score (GS) and cancer volume (CV), which are two important surrogate markers for prostate cancer aggressiveness. Currently, biopsy estimates GS based on architectural patterns of the sampled tissue at the microscopic level [1] and estimates CV mostly based on the percent positive biopsies. However, underestimations are sometimes observed mainly due to the sampling errors of biopsy [2-5]. This problem is partially alleviated in this paper, where we have developed optimized biopsy procedures that could differentiate between prostate specimens having high and low GS/CV by sampling the spatial cancer distributions at the macro level. Differentiation rates of 81.93% (for GS) and 94.79% (for CV) have been obtained under cross validation in a population of prostatectomy specimens. To the best of our knowledge, the optimized biopsy procedures are the first ones that use (macro-level) spatial cancer distributions to estimate GS and CV. More validations might be needed to reveal its generalization ability. Yangming Ou